Risk and Adaptation: Evidence from GlobalHurricane Damages and Fatalities

Laura A. Bakkensen∗ and Robert O. Mendelsohn†

Abstract

We examine whether countries adapt to hurricanes. A spatially re-fined global tropical cyclone data set is created to test for adaptation.We find evidence of adaptation in most of the world by examining theeffects of income, population density, and storm frequency on damageand fatalities. In contrast, there is no evidence of adaptation to dam-age in the United States leading to a damage function which is twentytimes higher than the rest of the world. (JEL D81, O1, O2, Q54, Q56,R5)

∗University of Arizona, laurabakkensen@email.arizona.edu†Yale University, robert.mendelsohn@yale.edu

1

Economists are well aware that people and firms adapt to risk. People use

smoke detectors for protection from residential fire (Dardis, 1980), seat belts

for protection from traffic accidents (Atkinson and Halvorsen, 1990), and

sunscreen for protection from ultraviolet rays (Dickie and Gerking, 1996).

But how much adaptation do individuals, firms, and governments already

undertake to cope with the risks of natural disasters? The expected annual

global damage from tropical cyclones (hurricanes) is $26 billion dollars plus

19,000 lives lost (Mendelsohn et al., 2012; CRED, 2012). Is this with or

without adaptation? If there is adaptation, how much damage and fatality

has been avoided?

In the absence of official government programs, the literature on tropical

cyclones often normalizes impacts. This implies that damage is proportional

to what is in harm’s way and increases proportionally with country-level

GDP (damage is proportional to both income and population) (Hsiang and

Narita, 2012; Nordhaus, 2010; Pielke et al., 2008; Pielke and Landsea, 1998).

This assumption is a natural extension of controlled experiments where dam-

age in a wind tunnel increases in unison with capital in harm’s way. This

also implies that adaptation is not effective in reducing damages. The liter-

ature is more ambiguous about fatalities, as there has long been evidence of

adaptation. However studies normalize fatalities, assuming fatality increases

proportionately with national population (Hsiang and Narita, 2012). But do

people, firms, and governments with property at risk and lives at stake take

no effective measures to protect their assets and themselves from catastrophic

2

events such as tropical cyclones? Or do people only protect themselves from

private risks such as fire and automobile accidents?

We test the adaptation hypothesis using two approaches. First, we es-

timate the income elasticity of damage and fatalities from tropical cyclones

around the globe. If the income elasticity of damage is unitary or if the

income elasticity of fatalities is zero, this would support the hypothesis of no

adaptation. We also calculate additional socioeconomic and cyclone elastici-

ties of impacts and compare with theoretical thresholds to test for additional

types of adaptation. Second, we compare tropical cyclone damage in the

United States to tropical cyclone damage in the rest of the world. The

United States receives an average of 4 percent of global landfalls but incurs

sixty percent of global damages1. We argue that there is no adaptation in

the United States because households, firms, and local governments are com-

pensated for economic damage from tropical cyclones by a combination of

subsidized national flood insurance, state regulations on coastal property in-

surance rates, and generous post disaster relief programs. Households, firms,

and local governments have virtually no incentive to adapt to the economic

damage from tropical cyclones in the United States. Such relief programs are

at much smaller scales in the rest of the world. The consequence of adapta-

tion can be measured by contrasting the damage from storms in the United

States with the damage in the rest of the world controlling for storm inten-

1Calculated by the authors using data from the Centre for Research on the Epidemiol-ogy of Disasters (CRED, 2012).

3

sity, population, and income. Of course, the United States does not have a

program that compensates for lives lost. So only the property damage and

not the fatalities from tropical cyclones are different in the United States.

We formally test the adaptation hypothesis by gathering spatially-explicit

data on 1,400 tropical cyclone landfalls from 1960 until 2010 that have struck

inhabited areas around the world. This represents the complete set of storms

for which damage and fatality impacts are publicly available2. We match

information about the strength of these storms as well as the income and

population density of the places where the hurricanes hit. We then regress the

observed damage and fatalities from these storms on the hurricane strength

as well as the population density and income of the affected area. We also

compute the underlying frequency of low and high intensity storm landfalls

for each area and explore to what degree prior experience affects the impacts

per storm.

We are not the first to tackle the question of adaptation. Several pa-

pers find evidence of adaptation in response to development and institutions

(Kahn, 2005; Toya and Skidmore, 2007; Kellenberg and Mobarak, 2007;

Fankhauser and McDermott, 2013), as well as the underlying risk of disas-

ter (Keefer, Neumayer, and Plumper, 2011; Schumacher and Stobel, 2011;

Hsiang and Narita, 2012). Additionally, a rich literature examines the im-

2Hsiang and Jina (2014) note there are 6,700 storms in recorded history after 1950.However, many do not make landfall and fewer still have records of damage and fatalityimpacts. We collect the complete set of storms that have publicly-available recordedimpacts.

4

pact of hurricanes on long-run economic growth (Skidmore and Toya, 2002;

Strobl, 2011; Cavallo et al., 2013; Hsiang and Jina, 2014). Cavallo and Noy

(2010) and Kousky (2012) provide informative review papers. Similar to the

existing literature, we only identify the benefits of adaptation and not the

costs or net benefits. However, previous literature has shown that at current

levels of adaptation, benefits likely far exceed costs in coastal areas (Yohe,

Neumann, and Ameden, 1995; Ng and Mendelsohn, 2005). Other studies

on hurricane effects include Hallstrom and Smith (2005) and Smith et al.

(2006).

We build upon this important work in several original ways. First, we offer

a comprehensive framework to approach the question of adaptation, extend-

ing to both damages and fatalities and covering multiple channels through

which adaptation is motivated (including level of development, population

density, storm characteristics, and underlying risk of storm landfalls). Sec-

ond, we collect the most spatially-refined global dataset to date. We include

socioeconomic data at the sub-country level and use a unit of observation at

the country-landfall, not country-year. This allows us to directly model dam-

ages and fatalities per storm, instead of modeling aggregated annual values.

It also insures that any missing data are excluded from the analysis instead

of assumed to be zero. Third, we find clear evidence that adaptation matters

and quantify the reduction in damages from adaptation. Unlike previous

work that finds adaptation only reduces long run damages and fatalities by

around 3 percent (Hsiang and Narita, 2012), we find it is greatly protective

5

and reduces impacts by more than an order of magnitude.

We find ample evidence of the benefits of adaptation to fatalities across

the world. The income elasticity to fatalities is quite negative. We also

find evidence of adaptation to economic damage in every country except the

United States. The income elasticity of damage in the rest of the world lies

between 0.6 and -2.3. All of these values are statistically significantly less

than unitary. In contrast, the income elasticity of damage in the US lies

between 1 and 1.6. The hurricane damage in the United States is about

20 times higher than the rest of the world per storm. If the United States

had the same damage coefficients as the rest of the world, the expected

annual American damage from hurricanes would be $0.8 billion instead of

the observed $15.3 billion. If the rest of the world had the same damage

coefficients as the United States, non-US global damage would be $208 billion

per year instead of the observed $10.4 billion. The results suggest that a

great deal of the potential damage from tropical cyclones has been eliminated

by adaptation, except in the United States.

1 Theory

Faced with a set of risks, individuals and governments often take steps to pro-

tect themselves. We define adaptation broadly, as any action that reduces the

expected damages or fatalities from a storm. Broad-scale improvements in

forecasting and tracking as well as advanced warning systems are included as

6

adaptation. Large infrastructural development in flood protection including

levees, river channelization, and beach nourishment are included. Further,

improvements in building codes, zoning ordinances, or individual activities

to strengthen homes, including hurricane shudders and stronger foundations,

all fall under the definition. Additional adaptive channels are not consciously

undertaken to protect against storms but are still included in our definition.

For example, if societies transition toward service-based economies through

development, thereby decreasing the supply-chain and inventory sensitivi-

ties to hurricanes, we consider this adaptive. Activities that would not fall

within our definition of adaptation include insurance, as this tool financially

protects the homeowner but does not decrease damages.

Adaptation drives a wedge between observed and potential damage and

fatalities (Brooks, 2003; Fankhauser and McDermott, 2013). To empiri-

cally identify this adaptive wedge between observed and potential losses, we

first characterize the distribution of human population and capital stock in

harm’s way. Gridded global population data are available (Dobson et al.,

2000; Bhaduri et al., 2002; CIESIN et al., 2005) but spatially-explicit census

data on the global capital stock across time are not available. Several proxy

databases exist (Nordhaus, 2006; De Bono, 2013) but the detailed variation

across space is driven mainly by population rather than income per capita

assumptions.

We follow the literature by predicting the capital stock from population

and income. We calculate the ratio between capital and per capita gross

7

domestic product (GDP per capita) to be 2.65 using 2005 country-level data

from the World Bank3. This is similar to the 2.8 value from Hallegate et

al. (2013) and the 3.1 value from Kamps (2004) but well below the 5 value

assumed by Hanson et al. (2011). Thus, the empirical evidence supports the

assumption that the capital stock scales proportionately with income and

population4. We consequently assume the per capita capital stock, K, is

K = 2.65Y

where Y is income per capita.

The potential damage per storm, PDx, is the damage expected in the

absence of adaptation. It is determined by the per capita capital stock, K,

the population struck by the storm, Pop, and the intensity of the storm, I.

Because we do not know the precise size of each storm, we proxy for the

population struck by that storm using the population density of the affected

location. We use two measures of intensity, minimum pressure and maximum

wind speed. We assume damage in the absence of adaptation to have the

following functional form:

PDx = α0Y PopIα3

Similarly, we assume that potential fatalities per storm, PFx, has the

3R2 value of 0.96.4Graphs and additional supporting evidence are available in the Appendix.

8

following functional form with respect to storm intensity, I, and population

density, Pop:

PFx = β0PopIβ3

Increases in population will lead to proportional increases in potential fa-

talities. With no adaptation, income does not enter the potential fatalities

function. People of every income are equally likely to die if nobody takes

precautions. The parameters, α and β, are assumed to be positive implying

an increase in any of the above factors are expected to increase potential

impacts, including increases in income (dPDdY

> 0), increases in population

density ( dPDdPop

> 0 and dPFdPop

> 0), and increases in storm intensity (dPDdI

> 0

and dPFdI

> 0).

We next assume that individuals choose some level of adaptation, A,

with benefit B(A) and cost C(A). Assuming that the adaptation benefit and

cost functions are well behaved, the optimal adaptation, A∗, occurs when

the marginal benefit equals the marginal cost, MB(A∗) = MC(A∗). We do

not assert that adaptation is necessarily efficient in this paper. We simply

test whether individuals, firms, and governments respond to higher levels of

benefits of adaptation by doing more adaptation. That is, we assume actors

choose some nonzero level of adaptation denoted by A1 based on the marginal

damage curve MD1 in Figure 1. With adaptation level A1, the total observed

damage equals the area of triangle A1EA3 whereas the total potential damage

(with no adaptation) is triangle 0MD1A3. The fraction of damage removed

9

to the potential damage, θ(A), is θ(A) = (0MD1EA1)/(0MD1A3). Note

that the removed damage is not the welfare gain of adaptation. The welfare

gain of adaptation A1 is the area below the MB1(A) and above MC(A)

curve, as one must subtract the adaptation cost5. Observed damage, Dx, is

the product of potential damages times the fraction of damage removed by

adaptation: Dx = θ(A) · PDx.

Figure 1: Marginal Costs and Marginal Benefits of Adaptation

Several factors can shift the MB(A) curve, from MB1(A) to MB2(A)

in Figure 1, impacting the level of potential and observed damages. The

5There terms can be equivalently defined by the following integrals:∫ A3

A1MB1(A)dA for

observed damage,∫ A3

0MB1(A)dA for potential damage, and

∫ A3

0MB1(A)dA−

∫ A3

A1MB1(A)dA∫ A3

0MB1(A)dA

for the adaptation impact θ(A).

10

marginal benefit of adaptation increases with income, population, storm in-

tensity, and underlying storm frequency (Π). Under an efficient solution, this

would also increase the equilibrium level of adaptation. However, we do not

require optimality, we simply test whether A2 > A1. That is, we test whether

adaptation increases as income, population density, or storm frequency in-

creases ( dAdY

> 0), ( dAdPop

> 0)6, ( dAdΠl

> 0) and ( dAdΠh

> 0). We specifically

examine the effect of predicted frequencies of both low (Πl) and high (Πh)

intensity storms. Incorporating potential demand shifters, we approximate

θ(A) with the following constant elasticity functional form:

θ(A) ≈ (1− γ0)Y −γ1Pop−γ2I−γ3x Π−γ4l Π−γ5

h

The γi terms equal zero if there is no adaptation. The observed damage will

have the following expression:

Dx = α0(1− γ0)Y 1−γ1Pop1−γ2Iα3−γ3x Π−γ4

l Π−γ5h

Similarly, observed fatalities, Fx, from storm x are the multiplicative product

of potential damages and adaptation, Fx = θ(A) · PFx:

Fx = β0(1− γ0)Y −γ1Pop1−γ2Iβ3−γ3x Π−γ4l Π−γ5

h

6This may be especially true if public adaptation is focused on areas with more people,but if adaptation costs increase in population, then there may be no increase in adaptation.

11

We test this proposition using both cross sectional evidence across loca-

tions as well as intertemporal variation in our panel. The study explores

whether adaptation increases as factors that would increase the potential

benefits of adaptation increase. If no adaptation is present in economic

damage and fatalities, then γi = 0 for i = {0, 1, 2, 3, 4, 5}. Whether γi > 0

is a testable hypothesis for the existence of adaptation. That is, adapta-

tion is present in economic damage to the extent that the income elasticity

and population elasticity are less than unitary (1). Adaptation would also be

evident if the historic frequency of storms lowers the damage per storm. Sim-

ilarly, adaptation is present in fatalities if the elasticity of income is negative,

the elasticity of population is less than one, or the elasticity with respect to

frequency is negative. Note however, that the potential coefficient on the

constant term and on the intensity of storms is not known and so cannot be

used to test for adaptation. Thus, relative comparisons within sample can

be made but there is not theoretical threshold based on our model.

Insurance is an important mechanism to cope with risks (Arrow, 1973;

Kunreuther, 1996). In some cases, insurance can be a substitute for adapta-

tion, especially when it is set at below actuarially-fair rates or coupled with

subsidized post-disaster aid (Kelly and Kleffner, 2003). Because the United

States has subsidized flood insurance (Michel-Kerjan, 2010), generous post

disaster compensation, and regulations on traditional insurance premiums

along the coast, individuals, firms, and local governments face very low costs

for being in harm’s way. Their insurance premiums for the additional risk

12

are near zero and they are often compensated for damage that is not in-

sured. Owners of both governmental and private capital along the coast in

the United States have no incentive to adapt to the risk of tropical cyclones.

There of course may be additional reasons why the United States is differ-

ent than the rest of the world. The US may have a stronger desire to live

along the coast and it may be wealthier. However, the study controls for

population density as well as income per capita at the county level.

By comparing the storm damage function of the United States versus the

rest of the world, one can test the assumption of the model parameters above.

We postulate that the damage function for the United States reflects potential

damage (zero abatement). The rest of the world exemplifies adaptation

since it does not offer such generous compensation programs. In contrast,

both the United States and the rest of the world should have similar fatality

functions. Another test of adaptation is therefore the difference between the

parameters of the United States damage function and the parameters of the

damage function for the rest of the world. The damage function of the

United States should resemble the potential damage function whereas the

damage function of the rest of the world resembles the adaptation damage

function. Note that we are not assuming perfectly efficient adaptation in the

rest of the world, just more extensive adaptation. Because the United States

does not compensate people for dying in a tropical cyclone, the United States

is not expected to have different fatality coefficients, just different damage

coefficients. The United States damage function also offers a good test of the

13

appropriate elasticities of the potential damage function. The United States

comparison also provides a test of the constant term and the coefficient on

storm intensity.

For all of these tests of adaptation, we are assuming that there are many

possible actors that can adapt including households, firms, and farms as well

as local and state governments. We are assuming that private actors focus

on reducing just their own damages, while governments focus on reducing

the damages to all the people in their jurisdiction. This analysis does not

distinguish who is doing the adaptation. We therefore are examining the

adaptation of both private individuals and firms as well as local govern-

ments. We do not know to what extent adaptation to economic damage is

a complement or substitute to adaptation to fatalities. Lastly, we do not

address the costs of adaptation and therefore do not calculate the net ben-

efits of adaptation. However, it is worth noting that the coastal protection

literature concerned with sea level rise finds that it is generally cheaper to

protect developed land than let it be inundated (Yohe et al., 1995; Ng and

Mendelsohn, 2005; Hunt et al., 2011; Neumann et al., 2011).

2 Empirical Strategy

Guided by the theoretical framework above, we use panel data to test for

the presence of adaptation to tropical cyclone damages and fatalities. We

14

first estimate damage and fatality functions using a log-log functional form

with cross-sectional and panel techniques7. Resulting estimated coefficients

can be interpreted as elasticities. We then test to see if these elasticities are

below theoretical thresholds (evidence of adaptation). We also test if adap-

tation levels vary across income levels by estimating respective damage and

fatality elasticities on partitioned samples including only low or high income

countries. This also reduces the potential for any strategic measurement er-

ror in the damage reports. Next, using our spatially refined data, we test for

the importance of local versus national adaptation. Finally, we compare the

elasticities of the United States to those of the rest of the world8.

We use both a cross-sectional model and error components model with

country and time fixed effects to calculate damage and fatality functions.

Cross-sectional analysis relies primarily on the variation across space to

identify parameters of interest, whereas the identifying variation for our

error components model relies on deviations from country and year aver-

ages. Broadly, cross-sectional results can shed light on long-run patterns

of adaptation (Mendelsohn et al., 1994). To the extent that some adapta-

tion changes very slowly over time, within-country and within-year variation

will not capture adaptive changes on these broader scales. However, cross-

sectional analysis may be confounded by time- and location-specific omitted

7Count data estimation and Seemingly Unrelated Regression model results are shown inthe Appendix. The results support the findings of our cross-sectional and error componentsmodels.

8See the Appendix for a detailed explanation of specification tests and explanatoryvariable choice.

15

variable bias that our error components model will subsume. Lastly, panel

data and cross-sectional results often have a different economic interpreta-

tion, as short term shocks are different than long term adaptive potential

(Timmins and Schlenker, 2009; Samuelson, 1947). Due to the strengths and

weaknesses of each technique, we present both models herein.

After specification and model selection testing presented in the Appendix,

we chose the following log-log functional form for its goodness of fit to model

damages for cyclone landfall j at time t in country i:

lnDijt = α0+α1lnYit+α2lnPopit+α3lnIijt+α4lnLijt+α5lnΠhi+α6lnΠli+αi+γt+uijt

and for fatalities:

lnFijt = β0+β1lnYit+β2lnPopit+β3lnIijt+β4lnLijt+β5lnΠhi+β6lnΠli+αi+γt+uijt

where Dij is direct economic damages and Fij is the number of fatalities.

These impacts are explained by Yit, the income per capita in country i at

the time of cyclone j; Popit, the population density; Iijt, the intensity of

cyclone j when making landfall in country i; Πli, the long-term frequency

of low intensity storms in country i; and Πhi, the long-term frequency of

high intensity storms in country i. Lij, a variable for landfall, is 1 if the

cyclone j made a direct landfall on the country i and otherwise equal to the

distance in kilometers of the storms’ closest approach. Since the variable

Lij is not present in our theoretical model, as a robustness check we also

16

drop this variable in the Appendix and find no change in the overall results.

In the error components model, we also include fixed effects for time (γt)

and country (αi). uijt is a mean-zero error term. Explanatory variables are

identical between the cross sectional and fixed effects specification except for

the year, γt, and country, αi, fixed effects which subsume the high and low

intensity cyclone frequency variables.

We estimate both functions using the Ordinary Least Squares (OLS) es-

timator. We also cluster standard errors at the country level in all specifica-

tions unless noted otherwise, to account for any within-country correlation

across error term observations9. While we include near misses in our main

result. In the Appendix we present results with near misses dropped, thereby

allowing our empirical model to exactly replicate our theoretical model. The

results do not change with the inclusion of near misses.

Unlike previous literature that aggregates up to the country-year level

(Hsiang and Narita, 2012; Neumayer, 2012; Noy, 2009; Kahn, 2005), one

major difference in our analysis is that our unit of observation is a country-

landfall (a storm striking a country). This means that a country suffering

from three storms in a year will be treated as three observations. There are

several advantages. First, this ensures that any missing storms or missing

data on storm impacts are not treated as zero, which could bias estimated

coefficients. Second, we directly model damages and fatalities at the storm

9Ferreira et al. (2013) note the importance of country-clustered standard errors forcross-country disaster analyses.

17

level. Thus, we can use more spatially refined data, including individual

storm characteristics at their point of landfall as well as sub-national socioe-

conomic controls. Lastly, we do not normalize cyclone impacts by population

or GDP, which would imply no adaptation. Using country level GDP or pop-

ulation to normalize for what is in harm’s way is problematic as each storm

hits a different place within a country with different characteristics.

In the Appendix, we present count data technique results for fatalities,

estimating semi-log regressions with the Negative Binomial estimator. We

test for and find evidence of over-dispersion in the data, implying that the

Negative Binomial estimator is preferred to the Poisson. We find that OLS

is appropriate when modeling the (log of) damages, as this variables follows

a normal distribution rather than a Poisson or Negative Binomial distribu-

tion. Fixed effects negative binomial results are included, but should be

interpreted with caution as there is still some debate in the literature as

to proper implementation of the fixed effect controls and what is actually

being subsumed by different implementations (Greene, 2007). The results

support the findings of our cross-sectional and fixed effects results. We also

use the Seemingly Unrelated Regression Model to potentially leverage effi-

ciency gains over OLS through exploitation of any correlation in the error

terms. However, we do not find this changes the main results and present

the results in the Appendix.

In addition to these main results, we do some robustness checks to test

selected sub-samples across: levels of development, spatial scales (national

18

versus local), and between the US and the rest of the world.

2.1 Data

For the empirical analysis, we build an original dataset of more than 1,400

storm landfalls around the Earth from 1960 to 2010 totaling almost $0.75

trillion in damages10 and approximately 400,000 lives lost. We begin our

analysis in 1960, coinciding with the start of satellites used for storm ob-

servations. Before this period, storms were incidentally observed by ships

and coastal communities, or found by aircraft flying long-range patterns in

search of disturbances (HRD, 2014). Thus, we have less confidence in the

accuracy of storm data and human impacts before this period. Hsiang and

Narita (2014) correctly note that 6,700 storms have been recorded by hu-

mans since 1950, but many of these storms do not make landfall and fewer

still can be linked with direct economic damages or human fatalities. Thus,

our dataset represents the full record of storms during our time period that

can be matched with publicly available damages and fatalities. We present

summary statistics in the Appendix.

Historical cyclone landfall damage and fatalities are from the EM-DAT

Emergency Disaster Database (CRED, 2012) and Nordhaus (2010) and are

matched with tropical cyclone characteristics compiled by NOAA IBTrACS

v03r03, U.S. Navy Tropical Cyclone Reports, and Nordhaus (2010). Both

maximum wind speed and minimum sea level pressure are tested as proxies

10All dollar values in this paper are in terms of real 2010 $USD.

19

for cyclone intensity. Additionally, we include the Power Dissipation Index

and Accumulated Cyclone Energy Index as cyclone intensity proxies in the

Appendix.

Ideally, analyses of damages and fatalities would control for the exact

population and capital impacted by the storm. However, the spatial extent

of a storm is not recorded by IBTrACS for most storms11. Thus, many studies

use country-level socioeconomic variables as proxies. We collect both coun-

try and sub-country data. We collect country-level population density and

per capita income data come from the Penn World Table v7.01, USDA ERS

International Macroeconomic Data, the CIA World Factbook, and Columbia

CIESIN’s Gridded Population of the World v3. In addition to national data

collected annually for the globe, we also collect sub-national, county-level

population density and income per capita data for six large countries (Aus-

tralia, China, India, Japan, Philippines, United States, and Mexico at the

state-level) using official census records. Storms to these six countries rep-

resent approximately 60 percent of our sample. The remaining countries

are small- to medium-sized whose national statistical more closely represent

the local levels. This allows us to more accurately assess the socioeconomic

conditions at landfall. Note, too, that by using income per capita (instead

of national GDP) and population density (instead of total population) we

can change spatial scale without impacting the overall level of damages. For

11The radius of maximum winds is recorded for a limited number of storms in theNorthern Atlantic.

20

example, smaller countries should have the same damage function as larger

countries. We test the importance of using country versus sub-country data

in the Results section. We also test both market exchange rate and purchas-

ing power parity definitions for income per capita and present our results in

the Appendix.

Since the size of the storm, its spatial extent, varies by storms but is

not accurately recorded, one cannot control for size in this study. We con-

sequently do not know the aggregate population or capital in harm’s way

of each storm. We approximate what is in harm’s way by using population

density and per capita income in the nearest counties to landfall. This allows

a much closer fit between where storms land and what is impacted than using

national GDP or population. Also, since our variables are per capita or per

square mile, we are also able to carefully test the benefits of using national

versus sub-national data in estimating damages.

Finally, a hurricane generator is used to predict the long-term frequency

for low and high intensity storm landfalls for each location. We turn to sim-

ulation data because the historical record of storm tracks is heterogeneous

in quality across time and space, especially before the development of the

Dvorjac technique that greatly improved accuracy in estimating hurricane

strength and the large-scale satellite deployment in the 1970s (Velden et

al., 2006). A total of 68,000 simulated cyclone tracks generated by Kerry

Emanuel are used to predict the frequencies by location (Emanuel et al.

2006; Mendelsohn et al., 2012). For the purposes of this analysis, low inten-

21

sity storms have 10-minute sustained maximum wind speeds that rank them

between a tropical depression and Category 3 strength (34 to 115 knots).

High intensity storms include all Category 4 and 5 storms (greater than 115

knots), based on wind speed (NHC, 2012). We present the summary statis-

tics of the sample in our Appendix. All together, 87 countries are struck by

tropical cyclones and are represented. Only observed landfalls are included

in the database, locations with no storms are omitted from our analysis.

With any data, measurement error is possible. In this analysis, there may

be measurement error with our estimates of damage (EM-DAT), income, and

cyclone intensity. All are addressed herein. The damage and fatality data

are likely the largest source of potential classical measurement error and

even strategic reporting bias. The bias introduced by strategic reporting

could impact accuracy in both directions: countries may try to under-report

damage to appear more capable, while other countries may try to over-report

damage to encourage international aid, relief, and sympathy. This could be

particularly true for lower income countries. Classical measurement error

will cause no bias in the regression coefficients but underreporting could bias

impacts downward. EM-DAT, the data provider, takes care to collect data

from multiple sources and verify the accuracy of the reports. If countries con-

sistently misreport data, then it would be observed during cross-verification

by EM-DAT from reports by the UN, World Bank, Red Cross, and other

organizations. EM-DAT prioritizes data from the most trusted sources. In

addition, we control for potential strategic reporting through selective sub-

22

sample regressions based on income, assuming that within income groups,

countries will not systematically differ in their incentives to miss-report. We

present our findings in the Results section and also the Appendix. We do

not find evidence that strategic reporting changes our results.

Income and GDP records may also have measurement error in report-

ing and estimation. We test a variety of data sources, including the Penn

World Table and USDA ERS International Macroeconomic Data, and both

market exchange rate and purchasing power parity definitions of GDP. We

also use our low versus high income partitioned regression results to address

potential measurement error concerns. Assuming the measurement error is

not consistent across data sources or within levels of development, similar

empirical results give us confidence that potential measurement error is not

a large factor biasing estimates.

Finally, measurement error is possible in the storm intensity record. Sci-

entific ability to accurately describe storm intensity has greatly improved in

the 1970s and 1980s with large scale satellite deployment and technique im-

provements (Velden et al., 2006). However, it is likely that minimum sea level

pressure is measured with greater accuracy than maximum wind speed (Gray

et al., 1991; and see our discussion in the Appendix and the Results section).

We minimize any potential measurement error by using multiple proxies for

storm intensity including maximum wind speed, barometric pressure, PDI,

and ACE. We find that minimum sea level pressure explains both damage

and fatalities more accurately than the alternative intensity measures.

23

Another important issue is that of selection of observations into our anal-

ysis sample. EM-DAT is arguably one of the best sources for global natural

disaster data available and verification of data quality is an important step

in their entry procedures (Tschoegl et al., 2006; Guha et al., 2002). However,

not all historical cyclone landfalls are included in the EM-DAT database and

not all cyclones in the database have a record of both damage and fatalities.

EM-DAT censors low impact storms with minimum damage and fatality cri-

terion12. However, the definition of a tropical cyclone itself censors storms

below a critical intensity. It is possible this censorship of small, low damage

storms affects the estimated parameters but this censorship is not expected

to have a large effect on the overall results because few fatalities and little

damage are caused by low intensity storms.

3 Results

This section presents our main results using cross-sectional and fixed effects

specifications. We find our results robust to alternative specifications, func-

tional forms, and additional sensitivity analyses. Our robustness checks are

presented in the Results section and detailed in the Appendix. Addition-

ally, in the Appendix, we drop near misses, presenting only the results for

which the distance from landfall equals zero. This empirical specification is

12A cyclone must meet at least one of the following criterion to be included in EM-DAT: 1) 10 or more fatalities, 2) 100 or more people affected, 3) a declaration of a stateof emergency, or 4) a call for international assistance (CRED, 2012).

24

identical to our theoretical model.

3.1 Fatalities

Table 1 shows the regression results for our fatality function using all coun-

tries. Columns 1, 2, and 3 are cross-sectional regressions. Column 1 presents

a basic regression. Column 2 decomposes the underlying cyclone frequency

into low, ΠL, and high, ΠH , intensity storms. Column 3 uses maximum wind

speed instead of minimum sea level pressure as a proxy for storm intensity.

Columns 4 and 5 add a year fixed effect. Columns 6 and 7 add a country

fixed effect. Note that the t-statistic on observed coefficients may be used

to test if estimated elasticities are statistically different from zero. We use

the F-test to test if relevant elasticities are statistically different from one.

The signs of the estimated elasticities are as we expected, with fatalities ris-

ing with lower minimum sea level pressure and higher maximum sustained

wind speed, I. Fatalities decrease as the distance from the eye of the storm

increases.

25

Tab

le1:

Evid

ence

ofA

dap

tati

onto

Fat

alit

ies

Dep

end

ent

Var

iab

le:

Log

Fat

alit

ies

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Reg

ress

ion

Bas

eΠ

Sp

lit

Win

dY

ear

FE

Yea

rF

E,

Win

dC

ountr

yF

EC

ou

ntr

yF

E,

Win

d

Ln

Inco

me

Per

Cap

ita

(Y)

-0.6

18**

*-0

.651

***

-0.6

53**

*-0

.618

***

-0.6

11**

*-0

.218

***

-0.1

35*

(0.0

834)

(0.0

886)

(0.0

871)

(0.0

868)

(0.0

863)

(0.0

738)

(0.0

684)

Ln

Pop

ula

tion

Den

sity

(Pop

)0.

146*

0.13

2*0.

106

0.14

5*0.

121

0.22

8***

0.2

24***

(0.0

786)

(0.0

772)

(0.0

817)

(0.0

870)

(0.0

910)

(0.0

509)

(0.0

694)

Ln

Inte

nsi

ty(Ix

Pre

ssu

re)

-9.1

89**

*-9

.429

***

-8.2

70**

*-1

0.54

***

(2.7

77)

(2.7

91)

(2.9

05)

(2.3

55)

Ln

Inte

nsi

ty(Ix

Win

dS

pee

d)

0.57

1***

0.38

4**

0.6

48***

(0.1

45)

(0.1

75)

(0.1

39)

Ln

Fre

qu

ency

All

(Π)

0.07

83*

(0.0

416)

Ln

Fre

qu

ency

Low

(ΠL

)0.

257*

*0.

248*

*0.

279*

**0.

273*

**(0

.103

)(0

.104

)(0

.099

6)(0

.099

6)L

nF

requ

ency

Hig

h(Π

H)

-0.1

18*

-0.1

20*

-0.1

35**

-0.1

31**

(0.0

673)

(0.0

670)

(0.0

653)

(0.0

643)

Ln

Lan

dfa

llD

ista

nce

(L)

-0.1

62**

*-0

.158

***

-0.1

57**

*-0

.149

***

-0.1

51**

*-0

.141

***

-0.1

39***

(0.0

231)

(0.0

227)

(0.0

222)

(0.0

227)

(0.0

232)

(0.0

216)

(0.0

219)

Con

stan

t69

.97*

**70

.86*

**3.

966*

**63

.35*

**4.

411*

**77

.76*

**1.8

41*

(19.

53)

(19.

67)

(1.1

40)

(20.

49)

(0.9

46)

(16.

36)

(0.9

86)

Yea

rF

EN

NN

YY

YY

Cou

ntr

yF

EN

NN

NN

YY

Ob

serv

atio

ns

1,00

61,

006

995

1,00

699

51,

020

1,0

08

R-s

qu

ared

0.23

50.

243

0.22

90.

297

0.29

00.

234

0.2

41

Not

e:**

*p<

0.01

,**

p<

0.05

,*

p<

0.1

All

spec

ifica

tion

sh

ave

stan

dar

der

rors

clu

ster

edat

the

cou

ntr

yle

vel.

26

Using our theoretical thresholds, we find strong evidence of adaptation

to fatalities. The income elasticity with respect to fatalities is less than

zero, β1 < 0, for all specifications, lying between -0.618 and -0.135. This

income elasticity of fatalities is consistent with the income elasticity of the

value of statistical life, found at the global meta-level to be between 0.5 to

0.6 (Viscusi and Aldy, 2003; Viscusi, 1993). We reject the null hypothesis

that the income elasticity is equal to zero for all specifications, and reject at

the 93% confidence level for the more conservative specification in column 7

where the elasticity is closest to zero. We also find evidence of adaptation

to fatalities with respect to population density, β2 < 1. Using the F-test, we

find that the estimated elasticities are all less than one at the 99% confidence

level. The population density coefficient implies that places with more people

suffer more fatalities. However, the coefficient also implies that the fatalities

per person are lower in more dense places. That is, from a personal point of

view, cities are safer than more rural areas. This may be a conscious urban

policy of adaptation for example due to urban evacuation plans or this result

could simply be a consequence of constructing dense and sturdy structures

in cities (Lindell et al., 2011; Whitehead, 2003).

We find a divided result for the underlying storm frequency. The coeffi-

cient on the frequency of high intensity storms elasticity is negative, β5 < 0,

implying places that are often hit by powerful storms have taken precautions.

Keefer et al. (2001) find similar results with lower fatalities to earthquakes

in areas hit more frequently. However, we find the opposite result for the fre-

27

quency of low intensity storms, (ΠL), as the estimated elasticities are greater

than zero, β4 > 0. This finding is significant at the 95% confidence level

in Column 2 through 5. Although this analysis does not specify the mal-

adaptive mechanism, one possible explanation is that individuals suffer from

warning fatigue. Frequent weak storms pose small risks that do not war-

rant dramatic responses. With frequent false alarms, people may stop taking

even modest precautions. Lastly, since people react differently to low and

high intensity storms, a variable characterizing overall frequency of storms,

Π, hides this dichotomous relationship. Thus, we caution the use of a single

variable to characterize underlying hazard risk commonly used in the litera-

ture (Fankhauser and McDermott, 2013; Hsiang and Narita, 2012; Neumayer

et al., 2013; Schumacher and Strobl, 2011; Keefer et al., 2011).

28

Tab

le2:

Evid

ence

ofA

dap

tati

onto

Dam

ages

Dep

end

ent

Var

iab

le:

Log

Dam

ages

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Reg

ress

ion

Bas

eΠ

Spli

tW

ind

Yea

rF

EY

ear

FE

,W

ind

Cou

ntr

yF

EC

ou

ntr

yF

E,

Win

d

Ln

Inco

me

Per

Cap

ita

(Y)

0.44

7**

0.42

0**

0.36

4**

0.40

3**

0.35

3**

0.02

70.1

23

(0.1

96)

(0.1

85)

(0.1

75)

-0.1

87(0

.175

)(0

.157

)(0

.169)

Ln

Pop

ula

tion

Den

sity

(Pop

)0.

074

0.05

7-0

.001

0.06

1-0

.034

-0.0

52-0

.303**

(0.1

28)

(0.1

26)

(0.1

54)

(0.1

26)

(0.1

54)

(0.2

07)

(0.1

33)

Ln

Inte

nsi

ty(Ix

Pre

ssu

re)

-29.

49**

*-2

9.94

***

-28.

40**

*-3

4.35

***

(6.2

69)

(6.0

61)

(5.2

88)

(7.3

08)

Ln

Inte

nsi

ty(Ix

Win

dS

pee

d)

1.86

9***

1.73

8***

1.9

97***

(0.3

83)

(0.4

12)

(0.4

89)

Ln

Fre

qu

ency

All

(Π)

-0.0

454

(0.1

01)

Ln

Fre

qu

ency

Low

(ΠL

)0.

169

0.23

9*0.

224

0.27

9**

(0.1

40)

(0.1

41)

(0.1

39)

(0.1

39)

Ln

Fre

qu

ency

Hig

h(Π

H)

-0.1

44-0

.170

*-0

.171

*-0

.189

*-0

.094

4-0

.097

8-0

.090

-0.0

957

Ln

Lan

dfa

llD

ista

nce

(L)

-0.4

14**

*-0

.413

***

-0.3

64**

*-0

.393

***

-0.3

49**

*-0

.360

***

-0.3

17***

(0.0

528)

(0.0

517)

(0.0

606)

(0.0

523)

(0.0

560)

(0.0

577)

(0.0

627)

Con

stan

t21

7.2*

**21

9.3*

**5.

879*

*20

8.9*

**6.

559*

*25

4.7*

**10.9

5***

(42.

63)

(41.

31)

(2.7

01)

(35.

44)

(2.9

28)

(49.

76)

(3.1

35)

Yea

rF

EN

NN

YY

YY

Cou

ntr

yF

EN

NN

NN

YY

Ob

serv

atio

ns

844

844

832

844

832

856

843

R-s

qu

ared

0.22

30.

227

0.21

20.

282

0.27

00.

246

0.2

33

Not

e:**

*p<

0.01

,**

p<

0.05

,*

p<

0.1

Sta

nd

ard

erro

rsar

ecl

ust

ered

atth

eco

untr

yle

vel.

29

3.2 Damage

Table 2 shows the results of the damage regressions using data from all coun-

tries. The column specifications are identical to those of Table 1. Damage

increases with the intensity of the storm13 and decreases with distance from

the eye. We find clear evidence of adaptation in the income elasticity with

respect to damage. The income elasticity varies from .03 to .45. The es-

timated income elasticities are all significantly less than one, α1 < 1. We

perform an F-test and reject (at the 99 percent confidence level) the unitary

income elasticity of damages assumed by the previous literature (Hsiang and

Narita, 2012; Nordhaus, 2010; Pielke et al., 2008; and Pielke and Landsea,

1998).

The coefficient on population density varies between -0.3 and 0.07. These

values are all significantly less than one, α2 < 1. As population density

increases, damages do not increase. This result indicates damage per person

falls in urban areas. Again this result may be due to conscious policies to

adapt urban areas to storms or it may simply be an incidental result of more

sturdy structures in urban areas.

Lastly, we find the elasticity of damage with respect to storm intensity

to be lower than past literature. For example, the elasticity of minimum

pressure is -29 to -34 whereas previous studies using data from the United

States found values of -86 (Mendelsohn et al., 2012). The elasticity of damage

13Recall that minimum sea level pressure has an inverse relationship with intensity; astronger storm has a lower pressure reading.

30

with respect to maximum sustained winds is from 1.7 to 2 which is much

closer to the traditional literature which found damage increases with the

second or third power of wind speed (Emanuel, 2005; Bell et al., 2000; Pielke

and Landsea, 1999). In contrast, the empirical results from US data imply

much higher elasticities of between 5 and 9 (Nordhaus, 2010; Mendelsohn et

al., 2012).

Based on the Vuong (1989), AIC, and BIC tests, we prefer the use of min-

imum sea level pressure over wind speed14. We also use the PDI and ACE as

additional proxies for storm intensity and present the results in the Appendix.

One explanation of the superiority of minimum pressure as a measure of in-

tensity is that wind speed may be measured with greater error than minimum

sea level pressure (Gray et al., 1991). Additionally, wind speed calculation

techniques have changed over time without good documentation whereas

minimum pressure reading techniques have remained consistent over time

(Emanuel, 2013). Maximum wind speed is calculated differently throughout

the world, reflecting 1-, 3-, or 10 minute sustained maximum wind speeds.

As there is no deterministic relationship between these different measures,

statistical averages must be used to convert them, leading measurements to

diverge from the true values. Finally, some wind speed estimates across the

world have been derived statistically from pressure readings whereas other

measures have relied on rules of thumb making it difficult to track the source

14We also test using both pressure and wind speed, but both variables become insignif-icant due to high multicollinearity.

31

of wind data (NRL, 1998). Thus, we recommend the use of minimum sea

level pressure readings to be utilized for tropical cyclone damage and fatality

research.

32

Tab

le3:

Adap

tati

onto

Fat

alit

ies:

Low

and

Hig

hIn

com

eC

ountr

ies

Dep

enden

tV

aria

ble

:L

ogF

atal

itie

s(1

)(2

)(3

)(4

)(5

)(6

)In

com

eL

evel

<$6

,500

<$6

,500

<$6

,500

>$2

0,00

0>

$20,

000

>$2

0,00

0R

egre

ssio

nB

ase

Dec

ade

FE

Cou

ntr

yF

EB

ase

Dec

ade

FE

Cou

ntr

yF

E

Ln

Inco

me

Per

Cap

ita

(Y)

-0.4

47**

*-0

.406

***

-0.0

01-2

.277

***

-2.7

48**

*-1

.751

**(0

.146

)(0

.131

)(0

.119

)(0

.369

)(0

.511

)(0

.621

)L

nP

opula

tion

Den

sity

(Pop

)0.

361*

**0.

372*

**0.

291*

**0.

134

0.14

60.

150*

*(0

.074

5)(0

.078

6)(0

.086

8)(0

.105

)(0

.089

4)(0

.068

7)L

nIn

tensi

ty(Ix

Pre

ssure

)-1

3.59

***

-11.

68**

*-1

3.01

***

-7.2

66-9

.116

*-2

.857

(2.6

92)

(2.6

16)

(2.8

94)

(5.4

69)

(5.0

91)

(3.3

30)

Ln

Lan

dfa

llD

ista

nce

(L)

-0.2

00**

*-0

.184

***

-0.1

60**

*-0

.146

***

-0.1

56**

*-0

.163

***

(0.0

204)

(0.0

213)

(0.0

223)

(0.0

394)

(0.0

324)

(0.0

472)

Con

stan

t98

.84*

**85

.69*

**92

.97*

**74

.47*

92.0

5**

39.5

1(1

8.93

)(1

8.16

)(2

0.46

)(3

8.65

)(3

6.80

)(2

4.39

)

Dec

ade

FE

NY

YN

YY

Cou

ntr

yF

EN

NY

NN

YO

bse

rvat

ions

579

579

579

152

152

152

R-s

quar

ed0.

184

0.20

90.

175

0.17

00.

210

0.13

1N

ote:

***

p<

0.01

,**

p<

0.05

,*

p<

0.1

All

spec

ifica

tion

shav

est

andar

der

rors

clust

ered

atth

eco

untr

yle

vel.

33

Tab

le4:

Adap

tati

onto

Dam

ages

:L

owan

dH

igh

Inco

me

Cou

ntr

ies

Dep

enden

tV

aria

ble

:L

ogD

amag

es(1

)(2

)(3

)(4

)(5

)(6

)In

com

eL

evel

<$6

,500

<$6

,500

<$6

,500

>$2

0,00

0>

$20,

000

>$2

0,00

0R

egre

ssio

nB

ase

Dec

ade

FE

Cou

ntr

yF

EB

ase

Dec

ade

FE

Cou

ntr

yF

E

Ln

Inco

me

Per

Cap

ita

(Y)

0.51

3***

0.60

6***

0.34

7*-1

.721

*-2

.311

*-2

.158

**(0

.178

)(0

.194

)(0

.204

)(0

.994

)(1

.118

)(0

.793

)L

nP

opula

tion

Den

sity

(Pop

)0.

405*

*0.

410*

*0.

003

0.00

8-0

.023

70.

510*

**(0

.166

)(0

.156

)(0

.076

3)(0

.175

)(0

.180

)(0

.147

)L

nIn

tensi

ty(Ix

Pre

ssure

)-2

4.33

***

-22.

77**

*-2

4.13

***

-37.

74**

*-3

8.20

***

-43.

23**

*(4

.132

)(3

.874

)(3

.421

)(1

2.15

)(1

3.31

)(1

1.44

)L

nL

andfa

llD

ista

nce

(L)

-0.3

94**

*-0

.398

***

-0.3

82**

*-0

.540

***

-0.5

43**

*-0

.659

***

(0.0

501)

(0.0

601)

(0.0

508)

(0.1

61)

(0.1

64)

(0.1

16)

Con

stan

t17

9.0*

**16

8.5*

**18

1.9*

**29

5.4*

**30

4.6*

**33

4.9*

**(2

8.33

)(2

6.66

)(2

3.73

)(8

0.96

)(9

1.61

)(7

5.92

)

Dec

ade

FE

NY

YN

YY

Cou

ntr

yF

EN

NY

NN

YO

bse

rvat

ions

414

414

414

145

145

145

R-s

quar

ed0.

230

0.25

10.

201

0.25

60.

278

0.28

4N

ote:

***

p<

0.01

,**

p<

0.05

,*

p<

0.1

All

spec

ifica

tion

shav

est

andar

der

rors

clust

ered

atth

eco

untr

yle

vel.

34

3.3 Adaptation Across Income Levels

One hypothesis that has been raised with respect to adaptation is that adap-

tive capacity rises with income. We test this hypothesis in Tables 3 and 4

by examining whether the income elasticity of damage and fatalities is lower

for higher income locations. We create sub-samples of the data for low in-

come (<$6,500) and high income (>$20,000) locations. We then estimate

separate regressions on each subsample. The United States is dropped as an

outlier in this analysis. Table 3 reveals the results for fatalities. The columns

vary depending upon the income of the locations and the use of fixed effects.

Columns 1 and 4 are OLS regressions, columns 2 and 5 have decade fixed ef-

fects and columns 3 and 6 have both time and country fixed effects. Standard

errors are clustered at the country level. To check the validity of the clustered

standard errors for subsample regressions with fewer than fifty bins, we also

calculate the coefficient p-values using wild bootstrapping as described by

Cameron et al. (2008) and implemented in Stata with Caskey (2013). The

significance of the results do not change. We use locations and not countries

for this analysis. The included high (low) income locations come mainly from

highly (lesser) developed countries and also wealthy urban centers (lower in-

come rural areas) in developing and lesser developed countries, controlling

also for population density. Therefore, differences are not driven by national

policies but location-specific differences between wealthy versus poorer areas.

Low income locations have an income elasticity with respect to fatalities

from 0 to -0.4 whereas high income locations have an income elasticity from

35

-1.8 to -2.7. These results provide strong support for the theory that people

adapt to prevent fatalities. Adaptation increases with income. The high

income location elasticities are statistically different from the elasticities of

low income locations at the 99% confidence level. These very negative income

elasticities of fatalities imply a much higher relationship between income and

value of life compared to the literature (Viscusi and Aldy, 2003; Viscusi,

1993). The remaining coefficients of the fatality model are not different for

the two subsamples.

Table 4 presents the damage results for low and high income locations.

The columns in each damage regression are identical to those in Table 3 for

fatalities. The income elasticity of damage for low income locations varies

between 0.35 and 0.61 whereas the income elasticity varies between -1.7 and

-2.3 for high income locations. All included countries show signs of adapta-

tion to economic damage, and once again the results imply that adaptation

increases rapidly with income, even overcoming the scale effect of more in

harm’s way. The damage income elasticity results are similar to the pro-

jections from an environmental Kuznets curve, with damages first increasing

and then decreasing with income (Shafik, 1994). The estimated population,

intensity, and distance coefficients are not statistically different between low

and high income countries.

36

3.4 Adaptation Across Space

Using the collected national and sub-national data, we then test adaptation

across levels of spatial scale. This allows us to estimate the gains from

using more nuanced spatial data. Sub-national data gives us insight into

local adaptation and the differences between adaptation in urban versus rural

areas. National socioeconomic data allows us to identify the broad differences

across levels of economic development and federal policies. Across space and

time, the county-level income per capita records are highly correlated with

the national levels (ρ = 0.94), while the population density is less correlated

across spatial scales (ρ = 0.75). Thus, within countries, the urban versus

rural population variation dominates the economic inequality across space.

In Table 5, we formally test the difference between national and sub-

national adaptation. Using the 60 percent of our data that has both national

and sub-national socioeconomic data, we estimate our impact functions us-

ing exclusively national-level data and then the same observations using sub-

national measures. The other 40 percent of the data includes many small

countries with less variation between local and national socioeconomic vari-

ables. Columns 1-4 show our fatality functions, while Columns 5-8 report

our estimated damage functions. Odd columns present county-level results

and even columns represent the equivalent regressions using national-level

socioeconomic data. Lastly, we drop the United States in Columns 3, 4, 7,

and 8. We highlight again that our population and income variables are nor-

malized by area and population, thus allowing us to navigate up and down

37

spatial scales without impacting the aggregate level of damages calculated.

For example, one landfall may be defined by a national per capita average in-

come of $15,000 and a local per capita average income of $20,000, but would

result in similar levels of estimated damages. These tests also allay fears of

our use of sub-national data in our main empirical results.

We find overall that both county- and national-level data have high ex-

planatory power for both damages and fatalities. Although there are few

statistically significant differences, the interpretation of results can yield po-

tentially important findings. Data from the national level shows higher rates

of adaptation, with slightly larger income elasticities of fatality (-0.87 versus

-0.91) and damage (-0.35 vs -0.50). This highlights the importance of the

overall level of development of a country allowing for better national-level

adaptation, potentially including efforts in advanced storm warning systems

or better coordination of post-disaster aid. Similarly, for the population den-

sity elasticity, damages show the strongest differences. Denser nations have

lower (more negative) elasticities than their more rural counterparts. Simi-

larly, within countries, rural areas have higher aggregate damages than urban

areas. This striking finding highlights the importance of public adaptation in

urban areas and also the potential difficulties protecting sparsely populated

areas.

38

Tab

le5:

Adap

tati

onA

cros

sSpat

ial

Sca

les

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Dep

enden

tV

aria

ble

(Ln)

Fat

alit

ies

Fat

alit

ies

Fat

alit

ies

Fat

alit

ies

Dam

ages

Dam

ages

Dam

ages

Dam

ages

Ln

Inco

me

Per

Cap

ita

(Cou

nty

)-0

.737

***

-0.8

74**

*0.

0589

-0.3

53**

*(0

.066

5)(0

.076

9)(0

.104

)(0

.122

)L

nIn

com

eP

erC

apit

a(N

atio

nal

)-0

.784

***

-0.9

05**

*-0

.298

***

-0.5

04**

*(0

.063

6)(0

.073

9)(0

.104

)(0

.111

)L

nP

opula

tion

Den

sity

(Cou

nty

)0.

0672

0.07

27-0

.265

***

-0.2

60**

*(0

.049

9)(0

.054

7)(0

.074

3)(0

.076

5)L

nP

opula

tion

Den

sity

(Nat

ional

)-0

.087

1*-0

.001

54-0

.727

***

-0.6

20**

*(0

.052

6)(0

.058

9)(0

.085

8)(0

.083

4)L

nIn

tensi

ty(P

ress

ure

)-1

6.03

***

-15.

26**

*-1

5.75

***

-15.

09**

*-2

8.26

***

-33.

46**

*-2

1.70

***

-24.

40**

*(2

.593

)(2

.557

)(2

.747

)(2

.703

)(4

.041

)(3

.799

)(4

.190

)(3

.934

)L

nL

andfa

llD

ista

nce

-0.1

71**

*-0

.168

***

-0.1

57**

*-0

.159

***

-0.4

99**

*-0

.408

***

-0.4

98**

*-0

.434

***

(0.0

323)

(0.0

323)

(0.0

337)

(0.0

336)

(0.0

673)

(0.0

648)

(0.0

634)

(0.0

608)

Ln

Cou

nt

Low

0.24

60.

556*

*0.

409

0.51

6**

2.68

0***

3.53

7***

3.05

4***

3.40

7***

(0.3

11)

(0.2

59)

(0.3

24)

(0.2

61)

(0.5

34)

(0.4

46)

(0.5

24)

(0.4

08)

Ln

Cou

nt

Hig

h-0

.252

**-0

.332

***

-0.3

69**

*-0

.413

***

-1.4

57**

*-1

.756

***

-1.7

27**

*-1

.824

***

(0.1

19)

(0.1

07)

(0.1

24)

(0.1

10)

(0.2

09)

(0.1

84)

(0.2

06)

(0.1

70)

Con

stan

t11

8.8*

**11

1.6*

**11

7.1*

**11

2.3*

**19

4.0*

**22

7.2*

**15

0.1*

**16

8.3*

**(1

8.37

)(1

7.94

)(1

9.49

)(1

8.99

)(2

8.59

)(2

6.32

)(2

9.46

)(2

7.36

)

Obse

rvat

ions

574

574

518

518

497

497

389

389

Spat

ial

Sca

leC

ounty

Nat

ional

Cou

nty

Nat

ional

Cou

nty

Nat

ional

Cou

nty

Nat

ional

USA

Incl

uded

?Y

YN

NY

YN

NR

-squar

ed0.

259

0.26

90.

267

0.27

80.

306

0.37

80.

342

0.41

3Sta

ndar

der

rors

inpar

enth

eses

**p<

0.01

,**

p<

0.05

,*

p<

0.1

39

3.5 The United States

The United States has been dropped as an outlier in earlier global studies

of tropical cyclone impacts (Hsiang and Narita, 2012). Here, we utilize the

economic damage results from the United States as an example of potential

damage (no adaptation). We first test to see if the United States is different

from the rest of the world, and then decompose the rest of the world into

highly and lesser developed nations to test for additional differences.

We first test to see if the estimated damage and fatality coefficients for the

United States are different than the rest of the world using both an F-test and

Chow Breakpoint test. We first conduct an F-test by including an indicator

variable for the United States (1 if USA, 0 otherwise). We then interact

this variable with the included variables, to allow for slope variation. We

then test if the sum of all US estimated coefficients is statistically different

from non-US coefficients. For damages, we calculate an F-statistic of 30.90,

rejecting the null hypothesis that the US and rest of the world coefficients

are equal to each other at more than the 0.1 percent level. For fatalities, we

calculate an F-statistic of 2.26 and fail to reject the null hypothesis that the

US and the rest of the world are the same at the 10 percent level.

We also conduct the Chow Breakpoint test to determine if the estimated

coefficients of subsamples of the entire sample are statistically different from

each other. It does not, however, test if the sample is optimally partitioned.

We perform the Chow breakpoint test on the USA and non-USA subsample

regressions. For the damage regressions, we calculate a chi-squared statistic

40

of 60.32, rejecting the null hypothesis at the 0.1 percent level that the co-

efficients of the explanatory variables are the same across sub-groups of the

data. For the fatalities regressions, we calculate a chi-squared statistic of

3.54 and fail to reject the null hypothesis at even the 40 percent level. Thus,

we conclude that the United States has a similar fatality function as the rest

of the world but a very different damage function.

Table 6 compares the regression results estimated by subsamples from the

United States, OECD countries excluding the United States, and non-OECD

countries. Results for both minimum pressure and wind speed are presented.

We find that the income elasticity of the United States varies between 1.1

and 1.6 which is consistent with the zero adaptation case. In contrast, the

income elasticity for the remaining countries in the OECD lies between -

0.5 and -0.6 and for the non-OECD countries between 0.2 and 0.3 which is

consistent with adaptation. The population density coefficient is negative for

the United States which is not consistent with the zero adaptation case. We

consequently conclude that the coefficient on population density is not a good

test of adaptation. A storm that strikes American cities causes less damage

than a storm that strikes rural areas. The discrepancy is more marked than

in any other country. The effect of storm intensity is higher in the United

States than the rest of the world. Damages escalate rapidly with intensity

in the United States. The elasticity with respect to minimum pressure is

-85 for the United States but -34 for the rest of the OECD and -24 for

non-OECD countries. Similar differences exist for wind speed. Finally, the

41

constant term is much higher for the United States implying that all storms

cause more damage.

How much higher would damages be across the globe if the rest of the

world did not adapt, and how much lower would damages be in the US if it

adapted like the rest of the world? We use our estimated impact function

to calculate these counter-factual scenarios. We use the US coefficients as

the example of zero adaptation and the rest of the world as the example of

adaptation. If the United States had the same damage coefficients as the

rest of the world, the annual tropical cyclone damages in the United States

would average $0.8 billion instead of the current $15.3 billion. If the rest

of the world had the same damage coefficients as the US, non-US global

damages would be $208 billion per year instead of the current $10.4 billion.

There is a 20 fold difference between the estimated damage function for the

US and the rest of the world. Thus, we find that adaptation matters greatly

in driving a large wedge between potential and observed impacts.

42

Tab

le6:

Unit

edSta

tes

Dam

ages

Dep

enden

tV

aria

ble

:L

ogD

amag

es(1

)(2

)(3

)(4

)(5

)(6

)C

ountr

ies

USA

USA

OE

CD

OE

CD

non

-OE

CD

non

-OE

CD

&non

-USA

&non

-USA

Reg

ress

ion

Bas

eW

ind

Bas

eW

ind

Bas

eW

ind

Ln

Inco

me

per

Cap

ita

1.14

8**

1.63

6***

-0.6

24-0

.459

0.28

5***

0.22

9**

(0.5

48)

(0.5

55)

(0.3

95)

(0.4

24)

(0.0

986)

(0.0

995)

Ln

Pop

ula

tion

Den

sity

-0.3

00-0

.342

0.29

8***

0.30

9**

0.09

800.

0677

(0.2

66)

(0.2

84)

(0.0

707)

(0.1

31)

(0.0

869)

(0.0

858)

Ln

MSL

P-8

4.75

***

-34.

35**

-23.

70**

*(7

.969

)(1

4.03

)(3

.312

)L

nM

axim

um

Win

d5.

069*

**2.

005

1.42

5***

(0.6

22)

(1.4

50)

(0.2

39)

Ln

Lan

dfa

llD

ista

nce

-0.1

35-0

.033

9-0

.690

***

-0.6

80**

*-0

.351

***

-0.3

22**

*(0

.300

)(0

.196

)(0

.144

)(0

.149

)(0

.042

7)(0

.043

4)C

onst

ant

592.

1***

-17.

07**

260.

0***

13.8

8*17

7.9*

**9.

737*

**(5

4.80

)(6

.796

)(9

7.12

)(7

.678

)(2

2.85

)(1

.261

)

Obse

rvat

ions

108

110

9581

653

652

R-s

quar

ed0.

498

0.44

60.

334

0.31

50.

171

0.15

5N

ote:

***

p<

0.01

,**

p<

0.05

,*

p<

0.1.

43

3.6 Robustness

We present our robustness results in the Appendix. We first provide evidence

of our capital formation assumption, showing that capital scales linearly with

changes in income and population. We also present our sensitivity analysis.

Our main empirical results are robust to alternative variables, functional

forms, and additional sensitivity analyses. We carefully test the shape of the

damage and fatality functions, including linear, log-linear, quadratic, and

cubic specifications. We test the impact of data definitions and additional

proxy variables through the use of both market exchange rate and purchas-

ing power parity income and GDP per capita. We also test different proxies

for storm intensity including maximum wind speed and minimum sea level

pressure, as well as both the Power Dissipation and Accumulated Cyclone

Energy indices. An additional robustness check drops “near misses”, or hur-

ricanes that do not directly make landfall in a country, presenting only the

results for which the distance from landfall equals zero. This empirical spec-

ification is identical to our theoretical model. We find that across all of these

specifications, our main results hold.

We also test different estimators, including the negative binomial esti-

mator and the Seemingly Unrelated Regressions (SUR) model. Since we

use identical regressors in both our equations, there is no efficiency gain in

the SUR model relative to the OLS model (Greene, 2003). We test the ap-

plicability of a cross-equation coefficient restriction, but find the estimated

coefficients in the damages and fatalities equations to be statistically differ-

44

ent from each other, thereby negating the motivation of imposing equality.

This makes theoretical sense; storm and human factors may impact damage

and fatalities in different and independent ways. We also provide additional

income bins and estimate income elasticities of damage and fatalities across

twelve levels of development. We lastly present results comparing fatalities

in the United States to that of the rest of the world. Across all of our ro-

bustness checks, we find additional confirmation of the conclusions from our

main empirical results.

4 Conclusion

This paper develops a theory of adaptation to tropical cyclone damages and

fatalities in order to test for the presence of adaptation. We add to the

literature by constructing a new, larger, and more spatially-explicit historical

dataset of more than 1,400 storms, matching cyclone landfall impacts with

spatially-refined socioeconomic and cyclonic characteristics. A set of multiple

regressions are then estimated with this new dataset to test for adaptation.

Two types of tests are undertaken. First, we look at economic damage and

explore whether the elasticity of income and to a lesser extent the elasticity of

population density is unitary. We also look at fatalities and explore whether

the elasticity of income is negative and whether the elasticity of population

is less than unitary. We also test if there is a negative relationship between

impacts and the underlying storm frequency. We decompose these main

45

findings into adaptation across level of development and spatial scale. We

find clear evidence of adaptation in most of these tests. The coefficient of

population density may not be a good test of adaptation. Nonetheless, the

damages and especially the fatalities are much less than one would expect if

no adaptation measures were being undertaken.

We also use economic damage in the United States as a counterfactual.

State but mostly federal policies compensate potential victims of hurricanes.

As a result, there is little private or local government incentive to adapt in the

United States. Compensation mechanisms in the rest of the world are much

smaller. Comparing the multiple regressions using just United States data,

other OECD countries, and non-OECD countries reveals the United States

has a unique damage function. The income elasticity of damage is unitary

or higher, the elasticity with respect to storm intensity is much higher, and

the constant is higher. If the United States had the damage function of the

rest of the world, expected damages from hurricanes would be twenty times

smaller. If the rest of the world had a damage function like the United States,

damages would be twenty times higher. Adaptation to tropical cyclones is

clearly very important.

Why is the United States an outlier? One major difference between the

United States and the rest of the world is the role of public policy in shap-

ing the incentives for coastal inhabitants to undertake risk. The incentive

to adapt to tropical cyclones has been virtually eliminated in the United

States. Coastal property is almost completely compensated for any risk from

46

hurricanes by several policies. Many states limit how high insurance rates

can climb for risky coastal areas effectively subsidizing high income house-

holds along the coast (Kousky, 2011). The National Flood Insurance Pro-

gram charges insurance premiums that are well below what they must pay,

especially when tropical cyclones strike. The U.S. Government Accountabil-

ity Office finds that historical program payouts exceed premiums by $30.4

billion (GAO, 2013). Post-disaster aid is funded through general tax rev-

enues across the country instead of being paid by premiums (Krutilla, 1966;

Kousky, 2010). The expectation of post disaster aid reduces adaptation

(Kelly and Kleffner, 2003). All of these policies encourage individuals to live

in more risky areas and take few precautions to protect property. Some of the

most rapidly developing areas in the United States are coastal, with limited

incentive to retreat to safer locations and no incentive to invest in physical

protection.

This research reveals adaptation to tropical cyclones is ongoing in most

of the world and it dramatically reduces damage and fatalities. However, the

study does not provide critical details about this adaptation. How much of

the adaptation is being done by private actors and how much by local, state,

and federal governments? How much adaptation is in hard structures such as

barriers and how much is just rational land use planning? What are the in-

centives to live by the sea relative to inland in the United States versus other

countries? Very little is known about the distribution of damages within a

tropical cyclone. How much of the damage is concentrated in low elevation

47

sites along the shore? How much is due to storm surge, high winds, or fresh

water flooding? More spatially detailed measures of storm characteristics,

what is in harm’s way, and damage are needed.

The research suggests several policy insights. We find that well-intentioned

compensation of victims through public programs decreases the incentive to

adapt. Specifically, federal programs have eliminated the incentive for pri-

vate individuals and firms as well as local and state governments to adapt to

reduce damage per storm. This leads to over-capitalization in high risk areas

that drive up aggregate observed damage and to the absence of adaptation

measures. The public insurance programs in the United States need reform.

Premiums need to cover the outlays for insurance to work properly. Fair

insurance premiums provide a useful signal to households, firms, and farms

informing them where risks are high and low. When public policies prevent

premiums from reflecting the true underlying risk, they eliminate this signal

causing private individuals and firms to make poor choices. Although a na-

tional post disaster compensation program serves a valuable compassionate

role, such programs can be paid for by assessing premiums on local and state

governments consistent with long term payouts in each jurisdiction. Fair pre-

miums provide a valuable incentive for governments to manage rather than

ignore these risks.

Second, the results suggest that there may be room to improve the public

communication of cyclone risks. Places with high intensity storms appear to

have taken measures to reduce fatalities per storm. Yet fatalities per storm

48

increase in places with many low intensity storms. It is unclear why fatali-

ties are higher in places with more frequent low intensity storms. Researchers

should explore whether public warnings can be improved to eliminate this

effect. Fatalities have fallen over time in most countries again suggesting an

effective adaptation program. However, the problem is not yet completely

solved. For example, 77 percent of global fatalities occurred in just two

countries, Myanmar and Bangladesh, over the last several decades15. It ap-

pears there are still at least two countries that can significantly improve their

overall performance.

Lastly, the results strongly suggest that economic development helps in-

crease adaptation to natural disasters. The income elasticity of damage is

less than one in every country except the United States. As countries de-

velop, the damage from tropical cyclones will be a smaller component of

their income. High income countries actually have lower aggregate damage.

There is also strong evidence that per capita damage is much lower in urban

areas. To the extent that development is both increasing incomes and urban-

ization, these factors will help reduce the future burden of tropical cyclones

on the economy. It is also quite clear that development is leading to fewer

fatalities. Both urbanization and rising incomes is cutting fatalities rapidly.

Development is a key component of adaptation to natural disasters.

15Calculated by the authors using data from CRED (2012).

49

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